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The results section of the research paper is where you report the findings of your study based upon the information gathered as a result of the methodology [or methodologies] you applied. The results section should simply state the findings, without bias or interpretation, and arranged in a logical sequence. The results section should always be written in the past tense. A section describing results [a.k.a., "findings"] is particularly necessary if your paper includes data generated from your own research.
When formulating the results section, it's important to remember that the results of a study do not prove anything . Research results can only confirm or reject the research problem underpinning your study. However, the act of articulating the results helps you to understand the problem from within, to break it into pieces, and to view the research problem from various perspectives.
The page length of this section is set by the amount and types of data to be reported . Be concise, using non-textual elements, such as figures and tables, if appropriate, to present results more effectively. In deciding what data to describe in your results section, you must clearly distinguish material that would normally be included in a research paper from any raw data or other material that could be included as an appendix. In general, raw data should not be included in the main text of your paper unless requested to do so by your professor.
Avoid providing data that is not critical to answering the research question . The background information you described in the introduction section should provide the reader with any additional context or explanation needed to understand the results. A good rule is to always re-read the background section of your paper after you have written up your results to ensure that the reader has enough context to understand the results [and, later, how you interpreted the results in the discussion section of your paper].
Bates College; Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Results . The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.
I. Structure and Approach
For most research paper formats, there are two ways of presenting and organizing the results .
NOTE: The discussion section should generally follow the same format chosen in presenting and organizing the results.
II. Content
In general, the content of your results section should include the following elements:
Using Non-textual Elements
III. Problems to Avoid
When writing the results section, avoid doing the following :
Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Caprette, David R. Writing Research Papers . Experimental Biosciences Resources. Rice University; Hancock, Dawson R. and Bob Algozzine. Doing Case Study Research: A Practical Guide for Beginning Researchers . 2nd ed. New York: Teachers College Press, 2011; Introduction to Nursing Research: Reporting Research Findings. Nursing Research: Open Access Nursing Research and Review Articles. (January 4, 2012); Reporting Research Findings. Wilder Research, in partnership with the Minnesota Department of Human Services. (February 2009); Results . The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Schafer, Mickey S. Writing the Results . Thesis Writing in the Sciences. Course Syllabus. University of Florida.
Why Don't I Just Combine the Results Section with the Discussion Section?
It's not unusual to find articles in social science journals where the author(s) have combined a description of the findings from the study with a discussion about their implications. You could do this. However, if you are inexperienced writing research papers, consider creating two sections for each element in your paper as a way to better organize your thoughts and, by extension, your paper. Think of the results section as the place where you report what your study found; think of the discussion section as the place where you interpret your data and answer the "so what?" question. As you become more skilled writing research papers, you may want to meld the results of your study with a discussion of its implications.
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Writing a scientific paper.
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Additional tips for results sections.
This is the core of the paper. Don't start the results sections with methods you left out of the Materials and Methods section. You need to give an overall description of the experiments and present the data you found.
A short article by Dr. Brett Couch and Dr. Deena Wassenberg, Biology Program, University of Minnesota
From: https://writingcenter.gmu.edu/guides/imrad-results-discussion
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Interpreting results from statistical analysis can be daunting, especially if you are unfamiliar with the field of statistics. However, understanding statistical results is crucial when you’re conducting quantitative research for your dissertation. In this blog post, we will outline a step-by-step guide to help you get started with interpreting the results of statistical analysis for your dissertation.
Before you start interpreting your statistical results, it is important to revisit your research questions and hypotheses. It is easy to be tempted to include as much information as possible, Doing so will ensure that you are interpreting your results in a way that answers your research questions. When initially confronted with the results of your statistical analyses, you may find it difficult to determine where to start. It is common to feel the temptation to include as much data as possible in your results chapter, fearing that excluding any information might compromise the integrity of the study. However, succumbing to this temptation can lead to a loss of direction and clarity in the presentation of results. Reviewing your research questions and hypotheses will help you to focus on the key findings that are relevant to your research objectives.
After reviewing your research questions and hypotheses (Step 1), the next crucial step in interpreting your statistical results is to examine your descriptive statistics. Descriptive statistics play a fundamental role in summarizing the basic characteristics of your data, providing valuable insights into its distribution, sample characteristics, frequencies, and potential outliers.
One aspect to consider when examining descriptive statistics is sample characteristics. These characteristics provide an overview of the participants or subjects included in your study. For example, in a survey-based study, you may examine demographic variables such as age, gender, educational background, or socioeconomic status. By analyzing these sample characteristics, you can understand the composition of your sample and evaluate its representativeness or any potential biases.
Additionally, descriptive statistics help you analyze the frequencies of categorical variables. Frequencies provide information about the distribution of responses or categories within a particular variable. This is particularly useful when examining survey questions with multiple response options or categorical variables such as occupation or political affiliation. By examining frequencies, you can identify dominant categories or patterns within your data, which may contribute to your overall understanding of the research topic.
Descriptive statistics allow you to explore additional measures beyond central tendency and dispersion. For example, measures such as skewness and kurtosis provide insights into the shape of your data distribution. Skewness indicates whether your data is skewed towards the left or right, while kurtosis measures the peakedness or flatness of the distribution. These measures help you assess the departure of your data from a normal distribution and determine if any transformation or adjustment is required for further analysis.
Analyzing descriptive statistics also involves considering any potential outliers in your data. Outliers are extreme values that significantly deviate from the majority of your data points. These data points can have a substantial impact on the overall analysis and conclusions. By identifying outliers, you can investigate their potential causes, assess their impact on your results, and make informed decisions about their inclusion or exclusion from further analysis.
Examining your descriptive statistics, including sample characteristics, frequencies, measures of distribution shape, and identification of outliers, provides a comprehensive understanding of your data. These insights not only facilitate a thorough description of your dataset but also serve as a foundation for subsequent analysis and interpretation.
After reviewing your research questions and hypotheses (Step 1) and examining descriptive statistics (Step 2), you need to understand the inferential statistics and determine their statistical significance.
Inferential statistics are used to draw conclusions and make inferences about a larger population based on the data collected from a sample. These statistical tests help researchers determine if the observed patterns, relationships, or differences in the data are statistically significant or if they occurred by chance. Inferential statistics involve hypothesis testing, which involves formulating a null hypothesis (H0) and an alternative hypothesis (Ha). The null hypothesis represents the absence of an effect or relationship, while the alternative hypothesis suggests the presence of a specific effect or relationship. By conducting hypothesis tests, you can assess the evidence in favor of or against the alternative hypothesis ( if you need a refresher on hypothesis testing – read more about it here ).
Statistical significance refers to the likelihood that the observed results are not due to random chance. It helps you determine if the findings in your study are meaningful and can be generalized to the larger population. Typically, a significance level (alpha) is predetermined (e.g., 0.05), and if the p-value (probability value) associated with the test statistic is less than the significance level, the results are deemed statistically significant.
By comprehending inferential statistics and assessing statistical significance, you can draw meaningful conclusions from your data and make generalizations about the larger population. However, it is crucial to interpret the results in conjunction with practical significance, considering the effect size, context, and relevance to your research questions and hypotheses.
It is important to note that statistical significance does not imply practical or substantive significance. Effect size or practical significance refers to the meaningfulness or importance of the observed effect or relationship in real-world terms. While a statistically significant result indicates that the observed effect is unlikely due to chance, it is essential to consider the magnitude of the effect and its practical implications when interpreting the results. They help you assess the importance and meaningfulness of the findings beyond mere statistical significance.
There are various effect size measures depending on the type of analysis and research design employed in your study. For example, in experimental or intervention studies, you might consider measures such as Cohen’s d or standardized mean difference to quantify the difference in means between groups. Cohen’s d represents the effect size in terms of standard deviations, providing an estimate of the distance between the group means.
In correlation or regression analyses, you may examine effect size measures such as Pearson’s r or R-squared. Pearson’s r quantifies the strength and direction of the linear relationship between two variables, while R-squared indicates the proportion of variance in the dependent variable explained by the independent variables.
Effect sizes are important because they help you evaluate the practical significance of your findings. A small effect size may indicate that the observed effect, although statistically significant, has limited practical relevance. Conversely, a large effect size suggests a substantial and meaningful impact in the context of your research.
Additionally, considering effect sizes allows for meaningful comparisons across studies. By examining effect sizes, researchers can assess the consistency of findings in the literature and determine the generalizability and importance of their own results within the broader scientific context.
It is worth noting that effect sizes are influenced by various factors, including sample size, measurement scales, and research design. Therefore, it is crucial to interpret effect sizes within the specific context of your study and research questions.
After reviewing your research questions and hypotheses (Step 1), examining descriptive statistics (Step 2), understanding inferential statistics and statistical significance (Step 3), and considering effect sizes (Step 4), the final step in interpreting your statistical results is to interpret them in the context of your research questions.
Interpreting your results involves drawing meaningful conclusions and providing explanations that align with your research objectives. Here are some key considerations for interpreting your results effectively:
Remember that interpreting your results is not a standalone process. It requires a holistic understanding of your research questions, data analysis techniques, and the broader context of your research field. Your interpretation should be logical, supported by evidence, and provide meaningful insights that contribute to the overall understanding of the research topic.
Here are some additional tips to help you interpret your statistical results effectively:
Interpreting the results of statistical analysis is a crucial step in any quantitative research dissertation. By following the steps outlined in this guide, you can ensure that you are interpreting your results in a way that answers your research questions. Remember to be cautious, objective, and clear when interpreting your results, and don’t hesitate to seek guidance from a statistician if you are struggling. With a little bit of practice and patience, you can unlock the insights hidden within your data and make meaningful contributions to your field of study.
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Many graduate students hear two important words a lot: "dissertation" and "thesis." These words can be confusing because they seem alike. But they're actually quite different, and knowing the difference can help you with your research and studies.
This guide will help explain dissertations and theses. We want to make things clear so you can feel more sure about your schoolwork. Let's look at the main differences between dissertations and theses to help you understand your school path better.
First, let's explain what is a dissertation vs thesis.
A thesis is a big research project usually needed for a master's degree. It shows a student’s knowledge about a subject and ability to do research. A thesis often looks at what other people have written and uses it to form their own ideas.
A dissertation is new research done for a doctoral degree. It tries to add new knowledge to a field of study. Dissertations need to be original, detailed and well-supported. They're often published as books or as several articles in academic journals.
Interestingly, these meanings can be different in different places. In the United States, people usually write dissertations for Ph.D. programs and theses for master's degrees. But in Europe, it's the other way around — dissertations are for master's programs, and theses are for Ph.D.s.
Now that we know the basics, let's look at the main differences between a thesis and a dissertation:
When working on these big projects, tools like Aithor can help organize your thoughts and keep your writing sounding natural and academic throughout your thesis or dissertation.
The difference between theses and dissertations can change depending on the school level:
At this level, a thesis is usually a focused study showing what a student can do with a specific topic. Dissertations are less common for undergraduates but might involve broader research.
A master's thesis shows a student knows a lot about a subject by looking at and combining existing research. A master's dissertation is less common but involves new research that adds to the field.
For Ph.D.s, the difference between a thesis and a dissertation is bigger. A doctoral thesis vs dissertation comparison shows that a dissertation involves a lot of research, collecting data, and creating new theories. It aims to make a big step forward in the field and is very important for getting a Ph.D.
Even though they're different, thesis versus dissertation comparisons also show some things that are the same:
Both usually have these parts:
Both are done with help from a teacher who guides the student through the research process.
If students find it hard to write so much for theses and dissertations, tools like Aithor can help keep long documents consistent and suggest ways to make the writing clearer and more academic.
Knowing the difference between thesis and dissertation is really important for graduate students. While they have some things in common, their different purposes and requirements make them distinct. Whether you're working on a thesis or a dissertation, remember that both let you show what you know and add to your field of study.
If you're worried about writing a thesis or dissertation, don't stress. Aithor can help you organize your ideas, build your arguments, and improve your writing. This can make the process easier and less stressful. With the right tools and attitude, you can write an impressive academic paper that you'll be proud of for a long time.
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The fastest (and smartest) way to craft a strong results section for your dissertation, thesis or research project.
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This template covers all the core components required in the results chapter of a typical dissertation, thesis or research project:
The purpose of each section is explained in plain language, followed by an overview of the key elements that you need to cover. The template also includes practical examples to help you understand exactly what’s required, along with links to additional free resources (articles, videos, etc.) to help you along your research journey.
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Home > ETD > Doctoral > 5927
An examination of employee's perceived level of their leader's religiosity as a moderator of the relationship between an employee's religiosity and job satisfaction.
Brad Carney , Liberty University Follow
School of Behavioral Sciences
Doctor of Philosophy in Psychology (PhD)
Benjamin Wood
religiosity, moderation, employee religiosity, job satisfaction, leader’s perceived level of religiosity
Leadership Studies | Religion
Carney, Brad, "An Examination of Employee's Perceived Level of Their Leader's Religiosity as a Moderator of the Relationship Between an Employee's Religiosity and Job Satisfaction" (2024). Doctoral Dissertations and Projects . 5927. https://digitalcommons.liberty.edu/doctoral/5927
The purpose of this study was to investigate whether an employee’s perceived level of their leader’s religiosity moderates the relationship between an employee’s level of religiosity and job satisfaction. The participants in this research study were recruited through the utilization of a snowball sampling method, primarily leveraging Liberty University’s doctoral student email list and social media platforms such as Facebook and LinkedIn. Participants in the study were required to be 18 and older and had been employed under their current leader for a minimum of one year. The total sample size was N=65. The researcher used a quantitative self-reporting survey approach to data collection using the Huber and Huber (2012) Centrality of Religiosity Scale (CRS-15) survey to measure a leader's level of religiosity as perceived by the employee and an employee's level of religiosity. The Spector (1985) Job Satisfaction Survey (JSS) was used to measure an employee's level of job satisfaction. The data collected from the online CRS-15 and JSS surveys was analyzed employing a correlation research design using linear regression with moderation analysis. The results did not show a significant moderating effect on an employee’s perceived level of their leader’s religiosity. Still, they did find that employees who perceived their leader to have a high level of religiosity reported higher levels of job satisfaction. Furthermore, this study is the first to investigate an employee’s perceived level of their leader’s religiosity and the effect it has on employee job satisfaction.
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2024-08-28T16:00:00+10:00
Lilly Hatwell from the Faculty of Medicine & Health won the 2024 UNSW 3 Minute Thesis Competition.
Photo: Isabella Moore.
PhD candidate Lilly Hatwell’s presentation on breast cancer treatments targeting the androgen receptor has won the 3MT competition and inaugural UNSW Founders Award.
UNSW’s innovative and world-changing doctoral research was on display at the popular 3 Minute Thesis (3MT) final on Tuesday evening.
This year’s 18 contestants distilled their 100,000-word PhD theses into bite-sized three-minute talks, delivered in front of a packed audience at Leighton Hall in the John Niland Scientia Building.
Topics ranged from modifying food prices to help improve diets, to using AI to improve transport planning, and the 16 traits that make for a charismatic leader.
The winner of the $3000 top prize was Lilly Hatwell from the Garvan Institute of Medical Research and UNSW Medicine & Health. She delivered an engaging presentation on game-changing tactics against breast cancer.
“I am incredibly honoured and thrilled to have received first prize and the UNSW Founders Award. The quality and diversity of research across UNSW is inspiring and it is such an exciting event to be a part of,” Lilly said.
Lilly’s presentation spoke about the ‘hormonal tug of war’ in the fight against the most common form of breast cancer. The hormone estrogen is already known to promote the proliferation of cancer cells, and as such, most current strategies target the estrogen receptor. However, many patients develop resistance to this type of treatment.
Her thesis flips the focus and targets a different hormone receptor – the androgen receptor – using a drug that increases its expression. This approach helps the androgen receptor suppress the tumour and steals genes that the estrogen receptor needs to drive tumour growth. Her research combines this drug with clinically available targeted therapies to hopefully identify effective combinations.
Lilly was also presented with the inaugural UNSW Founders Award that recognises the best story deserving to win an award.
She will now go on to represent UNSW at the Asia-Pacific 3MT Finals at the University of Queensland, as well as the International Universitas 21 competitions.
UNSW Vice-Chancellor and President, Professor Attila Brungs, extended his praise to all the PhD researchers who presented.
“My congratulations go out to all 3MT finalists for their ongoing dedication to their fascinating research. Each finalist demonstrated the diverse and forward-thinking research that UNSW is renowned for, and I have been left feeling incredibly inspired,” Prof. Brungs said.
“Lilly Hatwell’s presentation on breast cancer treatments targeting the androgen receptor combined with patient specific treatments was outstanding. She displayed exceptional knowledge in her field of research and communicated it to the audience in a clear and engaging manner.”
This year’s 3MT judges included Director of Marinya Capital and newspaper proprietor John Fairfax AO; Director of the Museum of Contemporary Art, Suzanne Cotter; Co-founder of The Daily Aus, Zara Seidler; UNSW Vice-Chancellor and President, Attila Brungs; and UNSW Deputy Vice-Chancellor of Research and Enterprise Professor Bronwyn Fox.
Professor Jonathan Morris, UNSW’s Pro Vice-Chancellor, Research Training and Dean of Graduate Research, said the highlight of this year’s 3MT competition was seeing the University’s talented PhD candidates demonstrate their ability to communicate their world class research.
“Our PhD candidates are engaged in high quality research and these presentations demonstrate the impact that their research has on society. The 3MT competition showcases their work in a really engaging and accessible way,” Prof. Morris said.
“I want to congratulate all our outstanding finalists for their efforts. This competition is the highlight of the higher degree research calendar and it’s one of the University’s premier events because it showcases the people who carry out the research and the quality of their work.”
Milad Mousavi from the Faculty of Engineering took second place with his innovative research presentation, ‘The Invisible Guardian of the Underground Galaxy’. His research addresses the many lives lost in the mining sector and aims to use a digital invisible guardian to predict accidents.
Third place was awarded to Ellen Bowden-Reid from The Kirby Institute and UNSW Medicine & Health for her presentation on RNA vaccines: the secret weapon against pandemic viruses.
The audience voted for their favourite 3MT in the People’s Choice award, which was awarded to Sari Puspita Dewi from the Faculty of Arts, Design & Architecture for her presentation on why Indonesian translation of academic textbooks is confusing and how it can be fixed to improve learning.
For enquiries about this story and interview requests please contact Yolande Hutchinson :
Tel: 0420 845 023 Email: [email protected]
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The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you've found in terms of the quantitative data you've collected. It presents the data using a clear text narrative, supported by tables, graphs and charts.
The results chapter of a thesis or dissertation presents your research results concisely and objectively. In quantitative research, for each question or hypothesis, state: The type of analysis used; Relevant results in the form of descriptive and inferential statistics; Whether or not the alternative hypothesis was supported
The statement of the results mustn't have any form of evaluation or interpretation. Negative results should be added in the findings section because they validate the results and provide high neutrality levels. The length of the dissertation findings chapter is an important question that must be addressed.
The results chapter in a dissertation or thesis (or any formal academic research piece) is where you objectively and neutrally present the findings of your qualitative analysis (or analyses if you used multiple qualitative analysis methods ). This chapter can sometimes be combined with the discussion chapter (where you interpret the data and ...
The results section is where you report the main findings of your research. Watch this video to learn how to report your results concisely and objectively in...
A well-structured results section enables readers to understand the progression of your experiments and the relationship between different findings. Begin by reminding readers of the research questions or hypotheses that guided your study. This alignment helps establish a clear connection between the objectives of your research and the ...
What should the thesis results section include? Include all relevant results as text, tables, or figures. Report the results of subject recruitment and data collection. For qualitative research, present the data from all statistical analyses, whether or not the results are significant. For quantitative research, present the data by coding or ...
The results chapter of a thesis or dissertation presents your research results concisely and objectively. In quantitative research, for each question or hypothesis, state: The type of analysis used. Relevant results in the form of descriptive and inferential statistics. Whether or not the alternative hypothesis was supported.
Conventionally, the results section is the fourth chapter of your dissertation, written after you present your method of study. How exactly you present your findings differs from study to study, depending on the topic and discipline your research is situated in, the methods you used, and what kind of data you are presenting.
When writing a dissertation or thesis, the results and discussion sections can be both the most interesting as well as the most challenging sections to write. You may choose to write these sections separately, or combine them into a single chapter, depending on your university's guidelines and your own preferences. ...
The easiest way to write a quantitative dissertation results section is to build it around a sub-question or hypothesis of your research. For each subquery, provide relevant results and include statistical analysis. Then briefly evaluate importance & reliability. Notice how each result relates to the problem or whether it supports the hypothesis.
Craft a convincing dissertation or thesis research proposal. Write a clear, compelling introduction chapter. Undertake a thorough review of the existing research and write up a literature review. Undertake your own research. Present and interpret your findings. Draw a conclusion and discuss the implications.
Reporting Research Results in APA Style | Tips & Examples. Published on December 21, 2020 by Pritha Bhandari.Revised on January 17, 2024. The results section of a quantitative research paper is where you summarize your data and report the findings of any relevant statistical analyses.. The APA manual provides rigorous guidelines for what to report in quantitative research papers in the fields ...
Guide contents. As part of the Writing the Dissertation series, this guide covers the most common conventions of the results and discussion chapters, giving you the necessary knowledge, tips and guidance needed to impress your markers! The sections are organised as follows: The Difference - Breaks down the distinctions between the results and discussion chapters.
Learn how to craft a rock-solid results chapter/section for your quantitative dissertation, thesis or research project. We explain each section of the typica...
The results chapter of a dissertation should include the core findings of a study. Essentially, only the findings of a specific study should be included in this section. These include: Data presented in graphs, tables, charts, and figures. Data collection recruitment, collection, and/or participants. Secondary findings like subgroup analyses ...
The results chapter of a thesis or dissertation presents your research results concisely and objectively. In quantitative research, for each question or hypothesis, state: The type of analysis used. Relevant results in the form of descriptive and inferential statistics. Whether or not the alternative hypothesis was supported.
The results section of the research paper is where you report the findings of your study based upon the information gathered as a result of the methodology [or methodologies] you applied. The results section should simply state the findings, without bias or interpretation, and arranged in a logical sequence. The results section should always be ...
Chris A. Mack. SPIE. 2018. Present the results of the paper, in logical order, using tables and graphs as necessary. Explain the results and show how they help to answer the research questions posed in the Introduction. Evidence does not explain itself; the results must be presented and then explained. Avoid: presenting results that are never ...
Avoid using absolute terms such as "These results prove that…", rather make use of terms such as "suggest" or "indicate", where you could say, "These results suggest that…" or "These results indicate…". It is highly unlikely that a dissertation or thesis will scientifically prove something (due to a variety of resource ...
The results section of a research paper tells the reader what you found, while the discussion section tells the reader what your findings mean. The results section should present the facts in an academic and unbiased manner, avoiding any attempt at analyzing or interpreting the data. Think of the results section as setting the stage for the ...
5 Steps to Interpreting Statistical Results for Your Dissertation: From Numbers to Insight Interpreting results from statistical analysis can be daunting, especially if you are unfamiliar with the field of statistics. However, understanding statistical results is crucial when you're conducting quantitative research for your dissertation. In this blog post, we will outline a step-by-step
A master's dissertation is less common but involves new research that adds to the field. Doctoral Level. For Ph.D.s, the difference between a thesis and a dissertation is bigger. A doctoral thesis vs dissertation comparison shows that a dissertation involves a lot of research, collecting data, and creating new theories.
This template covers all the core components required in the results chapter of a typical dissertation, thesis or research project: The purpose of each section is explained in plain language, followed by an overview of the key elements that you need to cover. The template also includes practical examples to help you understand exactly what's ...
The results did not show a significant moderating effect on an employee's perceived level of their leader's religiosity. Still, they did find that employees who perceived their leader to have a high level of religiosity reported higher levels of job satisfaction. Furthermore, this study is the first to investigate an employee's perceived ...
UNSW's innovative and world-changing doctoral research was on display at the popular 3 Minute Thesis (3MT) final on Tuesday evening. This year's 18 contestants distilled their 100,000-word PhD theses into bite-sized three-minute talks, delivered in front of a packed audience at Leighton Hall in the John Niland Scientia Building.